The use of sequence data is an increasingly important tool in understanding user behaviors with respect to interacting with electronic content (e.g., websites, software applications, etc.). For example, website visitors typically arrive on a website via a variety of different channels, and in addition, once on a website, visitors engage with the website in different ways manifest by different click paths that are captured and stored as sequence data. As such, sequence data can help a website manager accomplish a particular goal by using sequence data to understand how to adjust an advertisement campaign, alter the appearance of a webpage, change the placement of content, and so forth.
In another example, sequence data has the potential to help software developers optimize how users interact with a software application. For instance, sequence data can help a developer understand how users generally interact with an application, how users perform particular tasks within the application, and how often or what types of actions users take within the application. Based on these determinations, the developer can make various changes to the software application to make the application more user friendly, intuitive, and efficient.
Although the potential value provided by understanding sequence data is clear, conventional sequence analysis systems often fail to provide meaningful insight to a developer or manager because of the high-volume nature of sequence data. For example, a sequence dataset for a given period of time may include millions of unique events corresponding to thousands of distinct sequences, where each sequence includes hundreds of different ordered events. Conventional systems often produce statistics regarding the various sequences and/or individual events, but the conventional statistics typically fall short of providing meaningful insight with regard to such large datasets.
In an attempt to overcome providing meaningless statistics related to a given event sequence dataset, many conventional sequence analysis systems attempt to provide visualizations to illustrate insights based on the event sequence data. However, the volume and complexity of event sequence datasets render conventional visualization techniques inadequate for effective analysis. In sum, based on the volume and complexity of event sequence datasets, conventional systems often are not capable of providing a meaningful visualization that provides insight to allow developers and managers to make informed decisions.
Thus, a need exists to extract meaningful analytical data from large and complex event sequence datasets in a quick and efficient manner. Moreover, a need exists for an intuitive way to visualize large and complex event sequence datasets to generate and provide insightful and actionable information to website managers, software application developers, and others.